System and method for toboggan-based object segmentation using distance transform

ABSTRACT

A method of segmenting an object in a digital image comprising providing a digital image comprising a plurality of intensities corresponding to a domain of points in a N-dimensional space, selecting a region of interest in the image, determining a threshold intensity value for points in said region of interest, wherein an object of interest is defined by points with an intensity above a first pre-determined threshold, computing a distance map for each point in said object of interest, tobogganing each point in said object of interest based on said distance map, and selecting a cluster based on the results of said tobogganing.

CROSS REFERENCE TO RELATED UNITED STATES APPLICATIONS

This application claims priority from “Toboggan-based ObjectSegmentation using Distance Transform”, U.S. Provisional Application No.60/577,525 of Liang, et al., filed Jun. 7, 2004, the contents of whichare incorporated herein by reference.

TECHNICAL FIELD

This invention is directed to toboggan-based object segmentation indigital medical images.

DISCUSSION OF THE RELATED ART

The diagnostically superior information available from data acquiredfrom current imaging systems enables the detection of potential problemsat earlier and more treatable stages. Given the vast quantity ofdetailed data acquirable from imaging systems, various algorithms mustbe developed to efficiently and accurately process image data. With theaid of computers, advances in image processing are generally performedon digital or digitized images.

Digital images are created from an array of numerical valuesrepresenting a property (such as a grey scale value or magnetic fieldstrength) associable with an anatomical location points referenced by aparticular array location. The set of anatomical location pointscomprises the domain of the image. In 2-D digital images, or slicesections, the discrete array locations are termed pixels.Three-dimensional digital images can be constructed from stacked slicesections through various construction techniques known in the art. The3-D images are made up of discrete volume elements, also referred to asvoxels, composed of pixels from the 2-D images. The pixel or voxelproperties can be processed to ascertain various properties about theanatomy of a patient associated with such pixels or voxels.

The process of classifying, identifying, and characterizing imagestructures is known as segmentation. Once anatomical regions andstructures are identified by analyzing pixels and/or voxels, subsequentprocessing and analysis exploiting regional characteristics and featurescan be applied to relevant areas, thus improving both accuracy andefficiency of the imaging system. One method for characterizing shapesand segmenting objects is based on tobogganing. Tobogganing is anon-iterative, single-parameter, linear execution time over-segmentationmethod. It is non-iterative in that it processes each image pixel/voxelonly once, thus accounting for the linear execution time. The sole inputis an image's ‘discontinuity’ or ‘local contrast’ measure, which is usedto determine a slide direction at each pixel. One implementation oftobogganing uses a toboggan potential for determining a slide directionat each pixel/voxel. The toboggan potential is computed from theoriginal image, in 2D, 3D or higher dimensions, and the specificpotential depends on the application and the objects to be segmented.One simple, exemplary technique for defining a toboggan potential wouldbe as the intensity difference between a given pixel and its nearestneighbors. Each pixel is then ‘slid’ in a direction determined by amaximun (or minimum) potential. All pixels/voxels that slide to the samelocation are grouped together, thus partitioning the image volume into acollection of voxel clusters. Tobogganing can be applied to manydifferent anatomical structures and different types of data sets, e.g.CT, MR, PET etc., on which a toboggan type potential can be computed.

SUMMARY OF THE INVENTION

Exemplary embodiments of the invention as described herein generallyinclude methods and systems for toboggan-based object segmentation usinga distance transform (TBOS-DT). These methods include performing adistance transform to form a distance map, tobogganing with the distancemap as the toboggan potential, combining the formed toboggan clustersbased on the distance map, and extracting the objects of interest.According to one embodiment of the invention, a TBOS-DT for extractingpolyps in virtual colonoscopy includes computing a distance map,virtually sliding each voxels into its neighborhood based on thedistance map, collecting all voxels that converge to the same locationto form a toboggan cluster, and extracting polyps based on the formedtoboggan clusters.

According to an aspect of the invention, there is provided a method forsegmenting an object in a digital image, including providing a digitalimage comprising a plurality of intensities corresponding to a domain ofpoints in a N-dimensional space, computing a distance map for aplurality of points in said image, tobogganing each of said plurality ofpoints in said image based on said distance map, and selecting a clusterbased on the results of said tobogganing.

According to a further aspect of the invention, the method furthercomprises selecting a region of interest in the image, wherein saiddistance map is computed for points in said region of interest.

According to a further aspect of the invention, the method furthercomprises imposing a constraint on the intensity values of the points insaid region of interest, wherein an object of interest is defined bypoints that satisfy said constraint.

According to a further aspect of the invention, the method furthercomprises binarizing said region of interest based on said constraint,wherein pixels whose intensity value satisfy said constraint areassigned one binary value, and pixels whose intensity value do notsatisfy said constraint are assigned another binary value.

According to a further aspect of the invention, said constraint takesthe form of an inequality relationship between a pixel value and one ormore threshold values.

According to a further aspect of the invention, said distance map foreach point is determined by the distance of each point in said object ofinterest to a nearest point outside said object of interest.

According to a further aspect of the invention, said distance map is aEuclidean distance.

According to a further aspect of the invention, tobogganing each pointcomprises sliding each point towards a nearest neighbor point with alargest distance magnitude.

According to a further aspect of the invention, a point whose distancemagnitude is greater than that of its nearest neighbors is aconcentration location that does not slide.

According to a further aspect of the invention, a cluster is defined bya group of points that all slide to a same concentration location.

According to a further aspect of the invention, the method furthercomprises selecting a plurality of clusters, and merging said pluralityof clusters into a single cluster.

According to a further aspect of the invention, merging said pluralityof clusters includes selecting one of said plurality of clusters, andlabeling the points in said selected cluster with a set of labels,identifying surface points within the selected cluster, wherein asurface point is a point on a border of said object of interest,computing a centroid of the surface points, and adding to said set oflabels those labels corresponding to points within a preset distancefrom said centroid.

According to a further aspect of the invention, said steps ofidentifying surface points, computing a centroid, and adding to said setof labels are repeated until no new labels are added to the set oflabels, and further comprising extracting said object of interest asdefined by said surface points.

According to a further aspect of the invention, said inequality includesa first threshold value and a second threshold value greater than saidfirst threshold value, said object of interest is further defined bypoints with an intensity above said first threshold value and below asecond pre-determined threshold, and further comprising forming aternary map of said image, wherein pixels whose intensity is below saidfirst threshold are assigned a first ternary value, pixels whoseintensity is equal to or above said second threshold are assigned asecond ternary value, and pixels whose intensity is between said firstthreshold and said second threshold are assigned a third intensityvalue, wherein said distance is computed for those pixels correspondingto the third ternary value.

According to a further aspect of the invention, the distance map foreach point in said object of interest is determined by the distance ofeach point in said object of interest to a nearest point- with a firstternary value.

According to a further aspect of the invention, the method furthercomprises determining a distance threshold, and tobogganing only thosepixels in the object of interest whose distance map is less than thedistance threshold.

According to another aspect of the invention, there is provided aprogram storage device readable by a computer, tangibly embodying aprogram of instructions executable by the computer to perform the methodsteps for segmenting an object in a digital image.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 depicts a 2D artificial image created to resemble a smallcross-section colon with a polyp surrounded by lumen, according to anembodiment of the invention.

FIG. 2 depicts three orthogonal views for a real volume extracted from a3D CT image of colon, according to an embodiment of the invention.

FIG. 3 depicts a binarized version of the image of FIG. 1, with a giventhreshold, according to an embodiment of the invention.

FIG. 4 depicts a distance map based on applying the distance transformthe binary image in FIG. 3, according to an embodiment of the invention.

FIG. 5 depicts three orthogonal views illustrating a distance transformmap computed on the 3D volume shown in FIG. 2, according to anembodiment of the invention.

FIG. 6 shows the resulting clusters formed by the DT based tobogganing,according to an embodiment of the invention.

FIG. 7 depicts a final extracted polyp surface, according to anembodiment of the invention.

FIG. 8 depicts three orthogonal views that illustrate the result ofextracting the toboggan cluster as applied to the 3D volume from FIG. 2,according to an embodiment of the invention.

FIG. 9 depicts a flow chart of a toboggan-based method for polypsegmentation using distance transform, according to an embodiment of theinvention.

FIG. 10 is a block diagram of an exemplary computer system forimplementing a toboggan-based segmentation scheme according to anembodiment of the invention.

FIG. 11 depicts a flow chart of a toboggan cluster merger, according toan embodiment of the invention.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

Exemplary embodiments of the invention as described herein generallyinclude systems and methods for performing a toboggan-based objectsegmentation using a distance transform to find and characterize shapesin digital medical images. Although an exemplary embodiment of thisinvention is discussed in the context of segmenting and characterizingthe colon and in particular colon polyps, it is to be understood thatthe toboggan-based object segmentation methods presented herein haveapplication to 3D CT images, and to images from different modalities ofany dimensions on which a gradient field can be computed and toboggancan be performed.

Toboggan-based object segmentation using distance transform (TBOS-DT),starts with an object of interest has been located with a manual orautomatic procedure. According to an embodiment of the invention asapplied to segmenting polyps in virtual colonoscopy, a polyp candidatecan be manually clicked by a user with a mouse, or automaticallydetected by a detection software module. The output given by TBOS-DT arethe pixels that comprise the segmented object, which can be directlydisplayed to a user, or can serve as input to another module for furtherprocessing. Examples of further processing include computingmeasurements of the object, such as its longest linear dimension, itsvolume, moments of the intensity, etc. In other words, the first stepfor automated polyp measurement is polyp segmentation.

FIG. 9 depicts a flow chart of a toboggan-based method for polypsegmentation using distance transform according to an embodiment of theinvention. This embodiment will be discussed with respect to 2D and 3Dartificial images. At step 91, an object of interest or a region in animage is selected. FIG. 1 depicts a 2D artificial image created toresemble a small cross-section colon with a polyp surrounded by lumen.The polyp is represented by tissue values above 500 for this example,and the lumen is represented by the darker area, with tissue valuesbelow 500. FIG. 2 depicts three orthogonal views for a real volumeextracted from a 3D CT image of colon. The intersection of the dottedlines identifies an actual structure of interest.

At step 92, a base value is defined for the distance transformcomputation. The distance transform defines the distance for every pointin the image relative to a reference location. The reference locationscan be chosen with respect to one or more base or minimal values.According to an embodiment of the invention, the distance transform canbe computed with respect to an area, for example, relative to the lumenarea, and the image can be binarized into a lumen region and a non-lumenregion. The distance transform will be applied only to those pixels orvoxels in the non-lumen region. FIG. 3 depicts a binarized version ofthe image of FIG. 1, with a given threshold. In this example, thebinarization is obtained by thresholding with a value of 500. Everypixel below this threshold is regarded as lumen and is set to one andevery pixel above is set to zero. Those pixels whose intensity is abovethe threshold comprise an object of interest. Note that the assignmentof particular binary values is arbitrary and non-limiting, and pixelswith intensities below the threshold could be set to zero and thoseabove the threshold could be set to one.

At step 93, a distance map is computed based on the binarized imageshown in FIG. 3. The distance transform assigns each pixel in FIG. 3 anumber that is the distance between that pixel and the nearest nonzeropixel, yielding a distance map. FIG. 4 depicts a distance map based onapplying the distance transform to the binary image in FIG. 3. FIG. 5depicts three orthogonal views illustrating a distance transform mapcomputed on the 3D volume shown in FIG. 2. Referring to FIG. 5, lookingat the bottom right orthogonal view, the area toward the corner isbrighter since that point is the furthest from the lumen area. Noticealso a faint profile of the structure. In general, the distance can becomputed by treating the binarized image as a rectangular grid in anN-dimensional space. In the embodiments depicted herein, the grid can beconsidered as a 2D grid with coordinates (x, y), and the distance dbetween two pixels (x₁, y₁) and (x₂, y₂) can be defined according to theEuclidean distance d={square root}{square root over((x₁−x₂)²+(y₁−y₂)²)}. Similarly, a 3D image can be represented as a 3Dgrid with coordinates (x, y, x), and the distance between two voxels(x₁, y₁, z₁) and (x₂, y₂, z₂) is d={square root}{square root over((x₁−x₂)²+(y₁−y₂)²+(z₁−z₂)²)}. This distance metric is exemplary, andother distance metrics are within the scope of an embodiment of theinvention.

At step 94, tobogganing is performed on the distance transformed map.Each voxel in the volume slides/climbs to one of its nearest neighborsaccording to the computed potential. In general, the nearest neighborsof a pixel or voxel are those pixels/voxels that immediately surroundthe given pixel/voxel. For the embodiments of the invention disclosedherein, each 2D pixel will have 8 nearest neighbors, while each 3D voxelwill have up to 26 nearest neighbors. Note however, that for otherapplications and embodiments, diagonally oriented pixels could beexcluded from the nearest neighbor set. The selection of a neighbordepends on the application and the computation of toboggan potential. Inthe case of polyp segmentation, where the distance map is used as thetoboggan potential, the slide direction is determined by the neighborpixel with a maximal potential, that is, each voxel is climbing in thepotential. If a voxel has a higher potential than any of its neighbors,it does not climb further and becomes a concentration location. Thisprocess generates the toboggan direction and the toboggan label for eachvoxel for a given distance map. All the voxels that climb to the sameconcentration location are associated with a unique cluster label andgrouped into one toboggan cluster. FIG. 6 shows the resulting clustersformed by the DT based tobogganing. The arrows in the figure indicatethe sliding direction of the cluster pixels, while the circled pixelsare the concentration locations. Among them, there are 16 totalclusters, with 11 single-pixel clusters marked in dashed circles. Theconcentration location of each cluster is circled. Some of the clusterscontain only one pixel while others are larger. No tobogganing isperformed on pixels with zero potential.

According to another embodiment of the invention, the tobogganingprocess can be restricted to a local neighborhood based on a particularapplication. That is, it is not necessary for all the voxels toslide/climb in the sub-volume. For example, in case of polypsegmentation, only voxels in the region along the colon wall are ofinterest, and there is no need for a voxel in the air (or on the bone)to slide/climb. These voxels can be pre-thresholded out based on knownintensity values and related Houndsfield Units (HU) associated withlumen and bone.

For example, consider an embodiment of the invention with an image wherepixel intensity values below i₁ are known to be lumen, and pixelintensity values above i₂, where i₂>i₁, are known to be bone. This imagecould be transformed according to a ternary map, where pixels whoseintensities are less than i₁ are assigned value 0, those pixels withintensity greater than or equal i₁ but less than or equal i₂ areassigned value 1, and those pixels with intensity greater than i₂ areassigned value 2. The distance map could then be computed only on thosepixels with ternary value 1, based on their distance from a pixel withternary value 0.

More generally, according to another embodiment of the invention, anobject of interest can be determined by a constraint involving a pixel'sintensity value and one or more threshold values. These constraints candetermine multiple regions, one or more of which can be a region ofinterest. These constraints can most conveniently take the form of aninequality relationship between the pixel intensity value and the one ormore threshold values. In the binary case described above, theseconstraints can take the form of simple inequalities, such asintensity<threshold or intensity>threshold, or in the ternary case,threshold1<intensity<threshold2. The constraints can include compoundinequalities such as (threshold1<intensity<threshold2 ORthreshold3<intensity<threshold4). These examples are non-limiting, andin general, any Boolean expression comprising one or more relationalexpressions involving a pixel intensity and one or more thresholds,where multiple relational expressions are joined by logical operators,can be a constraint within the scope of an embodiment of the invention.

In addition, according to another embodiment of the invention, thedistance map can also be thresholded, so that any voxel with largerdistance than a chosen value is not processed. Thus, thresholding cannot only refine the areas to be processed but also remove unnecessarycomputation, thus accelerating the tobogganing process.

At step 95, the polyp is extracted by selecting the toboggan clusters.One toboggan cluster usually corresponds to an object of interest.However, there can be cases where the object of interest is broken intomultiple toboggan clusters and a merging strategy would be required.Basically, those toboggan clusters which together represent the objectof interest need to be merged into one big cluster. Various criteria canbe used for selecting toboggan clusters for merging. For example, thosetoboggan clusters concentrated within a certain distance from thedetection location can be selected. More sophisticated approaches, e.g.,one based on the student's t-test, can also be used.

FIG. 11 depicts a flow chart of a strategy for merging clusters,according to another embodiment of the invention. The merge starts atstep 111 by labeling pixels in a selected cluster with a list of labelsL. Based on the labels L, all surface voxels S can be easily identified112 based on the distance map. For example, referring to FIG. 6, thesurface pixels depicted therein are those whose distance transform valueis 1. Next, at step 113, compute the centroid C of the surface points S,and then at step 114 add to the cluster labels L all the labels L′within a predetermined distance from location C. The above steps ofidentifying surface pixels, computing a centroid, and adding labels to Lshould be repeated until 115 no new labels are added to L. The polyp canbe extracted 116 based on the final surface voxels S. FIG. 7 depicts afinal extracted polyp surface. Referring to the figure, those pixelsmarked with S identify the border (outer layer) of the toboggan cluster.Note also the clusters about the pixels in the two upper corners of thefigure. These are separated from the object of interest along the loweredge of the figure, and are thus not included in the merge with thatcluster. For simplicity only one such cluster merge is discussed. FIG. 8depicts three orthogonal views that illustrate the result of extractingthe toboggan cluster as applied to the 3D volume from FIG. 2. The objectof interest is indicated by the cluster of dots in the center of each ofthe three orthogonal views. Specifically, the outer layer (indicated byS in FIG. 7) is shown here with the white dots, and the internal pointson the cluster are indicated by the dark dots. Only one cluster is shownfor simplicity.

It is to be understood that the present invention can be implemented invarious forms of hardware, software, firmware, special purposeprocesses, or a combination thereof. In one embodiment, the presentinvention can be implemented in software as an application programtangible embodied on a computer readable program storage device. Theapplication program can be uploaded to, and executed by, a machinecomprising any suitable architecture.

Referring now to FIG. 10, according to an embodiment of the presentinvention, a computer system 101 for implementing the present inventioncan comprise, inter alia, a central processing unit (CPU) 102, a memory103 and an input/output (I/O) interface 104. The computer system 101 isgenerally coupled through the I/O interface 104 to a display 105 andvarious input devices 106 such as a mouse and a keyboard. The supportcircuits can include circuits such as cache, power supplies, clockcircuits, and a communication bus. The memory 103 can include randomaccess memory (RAM), read only memory (ROM), disk drive, tape drive,etc., or a combinations thereof. The present invention can beimplemented as a routine 107 that is stored in memory 103 and executedby the CPU 102 to process the signal from the signal source 108. Assuch, the computer system 101 is a general purpose computer system thatbecomes a specific purpose computer system when executing the routine107 of the present invention.

The computer system 101 also includes an operating system and microinstruction code. The various processes and functions described hereincan either be part of the micro instruction code or part of theapplication program (or combination thereof) which is executed via theoperating system. In addition, various other peripheral devices can beconnected to the computer platform such as an additional data storagedevice and a printing device.

It is to be further understood that, because some of the constituentsystem components and method steps depicted in the accompanying figurescan be implemented in software, the actual connections between thesystems components (or the process steps) may differ depending upon themanner in which the present invention is programmed. Given the teachingsof the present invention provided herein, one of ordinary skill in therelated art will be able to contemplate these and similarimplementations or configurations of the present invention.

The particular embodiments disclosed above are illustrative only, as theinvention may be modified and practiced in different but equivalentmanners apparent to those skilled in the art having the benefit of theteachings herein. Furthermore, no limitations are intended to thedetails of construction or design herein shown, other than as describedin the claims below. It is therefore evident that the particularembodiments disclosed above may be altered or modified and all suchvariations are considered within the scope and spirit of the invention.Accordingly, the protection sought herein is as set forth in the claimsbelow.

1. A method of segmenting an object in a digital image, comprising thesteps of: providing a digital image comprising a plurality ofintensities corresponding to a domain of points in a N-dimensionalspace; computing a distance map for a plurality of points in said image;tobogganing each of said plurality of points in said image based on saiddistance map; and selecting a cluster based on the results of saidtobogganing.
 2. The method of claim 1, further comprising selecting aregion of interest in the image, wherein said distance map is computedfor points in said region of interest.
 3. The method of claim 2, furthercomprising imposing a constraint on the intensity values of the pointsin said region of interest, wherein an object of interest is defined bypoints that satisfy said constraint.
 4. The method of claim 3, furthercomprising binarizing said region of interest based on said constraint,wherein pixels whose intensity value satisfy said constraint areassigned one binary value, and pixels whose intensity value do notsatisfy said constraint are assigned another binary value.
 5. The methodof claim 3, wherein said constraint takes the form of an inequalityrelationship between a pixel value and one or more threshold values. 6.The method of claim 1, wherein said distance map for each point isdetermined by the distance of each point in said object of interest to anearest point outside said object of interest.
 7. The method of claim 6,wherein said distance map is a Euclidean distance.
 8. The method ofclaim 1, wherein tobogganing each point comprises sliding each pointtowards a nearest neighbor point with a largest distance magnitude. 9.The method of claim 8, wherein a point whose distance magnitude isgreater than that of its nearest neighbors is a concentration locationthat does not slide.
 10. The method of claim 9, wherein a cluster isdefined by a group of points that all slide to a same concentrationlocation.
 11. The method of claim 1, further comprising selecting aplurality of clusters, and merging said plurality of clusters into asingle cluster.
 12. The method of claim 11, wherein merging saidplurality of clusters comprises the steps of: selecting one of saidplurality of clusters, and labeling the points in said selected clusterwith a set of labels; identifying surface points within the selectedcluster, wherein a surface point is a point on a border of said objectof interest; computing a centroid of the surface points; and adding tosaid set of labels those labels corresponding to points within a presetdistance from said centroid.
 13. The method of claim 12, wherein saidsteps of identifying surface points, computing a centroid, and adding tosaid set of labels are repeated until no new labels are added to the setof labels, and further comprising extracting said object of interest asdefined by said surface points.
 14. The method of claim 5, wherein saidinequality includes a first threshold value and a second threshold valuegreater than said first threshold value, said object of interest isfurther defined by points with an intensity above said first thresholdvalue and below a second pre-determined threshold, and furthercomprising forming a ternary map of said image, wherein pixels whoseintensity is below said first threshold are assigned a first ternaryvalue, pixels whose intensity is equal to or above said second thresholdare assigned a second ternary value, and pixels whose intensity isbetween said first threshold and said second threshold are assigned athird intensity value, wherein said distance is computed for thosepixels corresponding to the third ternary value.
 15. The method of claim14, wherein the distance map for each point in said object of interestis determined by the distance of each point in said object of interestto a nearest point with a first ternary value.
 16. The method of claim1, further comprising determining a distance threshold, and tobogganingonly those pixels in the object of interest whose distance map is lessthan the distance threshold.
 17. A method of segmenting an object in adigital image, comprising the steps of: providing a digital imagecomprising a plurality of intensities corresponding to a domain ofpoints in a N-dimensional space; determining a threshold intensity valuefor points in said image, wherein points with an intensity above apre-determined threshold define an object of interest; computing adistance map for each point in said object of interest; tobogganing eachpoint in said object of interest based in said distance map by slidingeach point towards a nearest neighbor point with a largest distancemagnitude, wherein a point whose distance magnitude is greater than thatof its nearest neighbors is a concentration location that does notslide.
 18. The method of claim 17, wherein a cluster is defined by agroup of points that all slide to a same concentration location, andfurther comprising selecting a cluster for further analysis.
 19. Aprogram storage device readable by a computer, tangibly embodying aprogram of instructions executable by the computer to perform the methodsteps for segmenting an object in a digital image, said methodcomprising the steps of: providing a digital image comprising aplurality of intensities corresponding to a domain of points in aN-dimensional space; computing a distance map for a plurality of pointsin said image; tobogganing each of said plurality of points in saidimage based on said distance map; and selecting a cluster based on theresults of said tobogganing.
 20. The computer readable program storagedevice of claim 19, the method further comprising selecting a region ofinterest in the image, wherein said distance map is computed for pointsin said region of interest.
 21. The computer readable program storagedevice of claim 20, the method further comprising imposing a constrainton the intensity values of the points in said region of interest,wherein an object of interest is defined by points that satisfy saidconstraint.
 22. The computer readable program storage device of claim21, the method further comprising binarizing said region of interestbased on said constraint, wherein pixels whose intensity value satisfysaid constraint are assigned one binary value, and pixels whoseintensity value do not satisfy said constraint are assigned anotherbinary value.
 23. The computer readable program storage device of claim21, wherein said constraint takes the form of an inequality relationshipbetween a pixel value and one or more threshold values.
 24. The computerreadable program storage device of claim 19, wherein said distance mapfor each point is determined by the distance of each point in saidobject of interest to a nearest point outside said object of interest.25. The computer readable program storage device of claim 24, whereinsaid distance map is a Euclidean distance.
 26. The computer readableprogram storage device of claim 19, wherein tobogganing each pointcomprises sliding each point towards a nearest neighbor point with alargest distance magnitude.
 27. The computer readable program storagedevice of claim 26, wherein a point whose distance magnitude is greaterthan that of its nearest neighbors is a concentration location that doesnot slide.
 28. The computer readable program storage device of claim 27,wherein a cluster is defined by a group of points that all slide to asame concentration location.
 29. The computer readable program storagedevice of claim 19, the method further comprising selecting a pluralityof clusters, and merging said plurality of clusters into a singlecluster.
 20. The computer readable program storage device of claim 29,wherein merging said plurality of clusters comprises the steps of:selecting one of said plurality of clusters, and labeling the points insaid selected cluster with a set of labels; identifying surface pointswithin the selected cluster, wherein a surface point is a point on aborder of said object of interest; computing a centroid of the surfacepoints; and adding to said set of labels those labels corresponding topoints within a preset distance from said centroid.
 31. The computerreadable program storage device of claim 30, wherein said steps ofidentifying surface points, computing a centroid, and adding to said setof labels are repeated until no new labels are added to the set oflabels, and further comprising extracting said object of interest asdefined by said surface points.
 32. The computer readable programstorage device of claim 23, wherein said inequality includes a firstthreshold value and a second threshold value greater than said firstthreshold value, said object of interest is further defined by pointswith an intensity above said first threshold value and below a secondpre-determined threshold, and further comprising forming a ternary mapof said image, wherein pixels whose intensity is below said firstthreshold are assigned a first ternary value, pixels whose intensity isequal to or above said second threshold are assigned a second ternaryvalue, and pixels whose intensity is between said first threshold andsaid second threshold are assigned a third intensity value, wherein saiddistance is computed for those pixels corresponding to the third ternaryvalue.
 33. The computer readable program storage device of claim 32,wherein the distance map for each point in said object of interest isdetermined by the distance of each point in said object of interest to anearest point with a first ternary value.
 34. The computer readableprogram storage device of claim 19, the method further comprisingdetermining a distance threshold, and tobogganing only those pixels inthe object of interest whose distance map is less than the distancethreshold.